---
title: Quickstart
---

import AlphaCallout from "/snippets/alpha-lg-callout.mdx";

<AlphaCallout />

Using the Graph API:
:::python

```python
# Step 0: Define tools and model

from langchain_core.tools import tool
from langchain.chat_models import init_chat_model

llm = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    temperature=0
)


# Define tools
@tool
def multiply(a: int, b: int) -> int:
    """Multiply a and b.

    Args:
        a: first int
        b: second int
    """
    return a * b


@tool
def add(a: int, b: int) -> int:
    """Adds a and b.

    Args:
        a: first int
        b: second int
    """
    return a + b


@tool
def divide(a: int, b: int) -> float:
    """Divide a and b.

    Args:
        a: first int
        b: second int
    """
    return a / b


# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
llm_with_tools = llm.bind_tools(tools)

# Step 1: Define state

from langchain_core.messages import AnyMessage
from typing_extensions import TypedDict, Annotated
import operator

class MessagesState(TypedDict):
    messages: Annotated[list[AnyMessage], operator.add]
    llm_calls: int

# Step 2: Define model node
from langchain_core.messages import SystemMessage
def llm_call(state: dict):
    """LLM decides whether to call a tool or not"""

    return {
        "messages": [
            llm_with_tools.invoke(
                [
                    SystemMessage(
                        content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
                    )
                ]
                + state["messages"]
            )
        ],
        "llm_calls": state.get('llm_calls', 0) + 1
    }


# Step 3: Define tool node


from langchain_core.messages import ToolMessage

def tool_node(state: dict):
    """Performs the tool call"""

    result = []
    for tool_call in state["messages"][-1].tool_calls:
        tool = tools_by_name[tool_call["name"]]
        observation = tool.invoke(tool_call["args"])
        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
    return {"messages": result}

# Step 4: Define logic to determine whether to end

from typing import Literal
from langgraph.graph import StateGraph, START, END

# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
def should_continue(state: MessagesState) -> Literal["tool_node", END]:
    """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""

    messages = state["messages"]
    last_message = messages[-1]
    # If the LLM makes a tool call, then perform an action
    if last_message.tool_calls:
        return "tool_node"
    # Otherwise, we stop (reply to the user)
    return END

# Step 5: Build agent

# Build workflow
agent_builder = StateGraph(MessagesState)

# Add nodes
agent_builder.add_node("llm_call", llm_call)
agent_builder.add_node("tool_node", tool_node)

# Add edges to connect nodes
agent_builder.add_edge(START, "llm_call")
agent_builder.add_conditional_edges(
    "llm_call",
    should_continue,
    ["tool_node", END]
)
agent_builder.add_edge("tool_node", "llm_call")

# Compile the agent
agent = agent_builder.compile()


from IPython.display import Image, display
# Show the agent
display(Image(agent.get_graph(xray=True).draw_mermaid_png()))

# Invoke
from langchain_core.messages import HumanMessage
messages = [HumanMessage(content="Add 3 and 4.")]
messages = agent.invoke({"messages": messages})
for m in messages["messages"]:
    m.pretty_print()

```

:::

:::js

```typescript
// Step 0: define tools and model
import { ChatAnthropic } from "@langchain/anthropic";
import { tool } from "@langchain/core/tools";
import * as z from "zod";

const llm = new ChatAnthropic({
  model: "claude-3-7-sonnet-latest",
  temperature: 0,
});

// Define tools
const add = tool(({ a, b }) => a + b, {
  name: "add",
  description: "Add two numbers",
  schema: z.object({
    a: z.number().describe("First number"),
    b: z.number().describe("Second number"),
  }),
});

const multiply = tool(({ a, b }) => a * b, {
  name: "multiply",
  description: "Multiply two numbers",
  schema: z.object({
    a: z.number().describe("First number"),
    b: z.number().describe("Second number"),
  }),
});

const divide = tool(({ a, b }) => a / b, {
  name: "divide",
  description: "Divide two numbers",
  schema: z.object({
    a: z.number().describe("First number"),
    b: z.number().describe("Second number"),
  }),
});

// Augment the LLM with tools
const toolsByName = {
  [add.name]: add,
  [multiply.name]: multiply,
  [divide.name]: divide,
};
const tools = Object.values(toolsByName);
const llmWithTools = llm.bindTools(tools);

// Step 1: Define state
import { StateGraph, START, END } from "@langchain/langgraph";
import { MessagesZodMeta } from "@langchain/langgraph";
import { registry } from "@langchain/langgraph/zod";
import { type BaseMessage } from "@langchain/core/messages";

const MessagesState = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
  llmCalls: z.number().optional(),
});

// Step 2: Define model node
import { SystemMessage } from "@langchain/core/messages";
async function llmCall(state: z.infer<typeof MessagesState>) {
  return {
    messages: await llmWithTools.invoke([
      new SystemMessage(
        "You are a helpful assistant tasked with performing arithmetic on a set of inputs."
      ),
      ...state.messages,
    ]),
    llmCalls: (state.llmCalls ?? 0) + 1,
  };
}

// Step 3: Define tool node
import { isAIMessage, ToolMessage } from "@langchain/core/messages";
async function toolNode(state: z.infer<typeof MessagesState>) {
  const lastMessage = state.messages.at(-1);

  if (lastMessage == null || !isAIMessage(lastMessage)) {
    return { messages: [] };
  }

  const result: ToolMessage[] = [];
  for (const toolCall of lastMessage.tool_calls ?? []) {
    const tool = toolsByName[toolCall.name];
    const observation = await tool.invoke(toolCall);
    result.push(observation);
  }

  return { messages: result };
}

// Step 4: Define logic to determine whether to end
async function shouldContinue(state: z.infer<typeof MessagesState>) {
  const lastMessage = state.messages.at(-1);
  if (lastMessage == null || !isAIMessage(lastMessage)) return END;

  // If the LLM makes a tool call, then perform an action
  if (lastMessage.tool_calls?.length) {
    return "toolNode";
  }

  // Otherwise, we stop (reply to the user)
  return END;
}

// Step 5: Build and compile the agent
const agent = new StateGraph(MessagesState)
  .addNode("llmCall", llmCall)
  .addNode("toolNode", toolNode)
  .addEdge(START, "llmCall")
  .addConditionalEdges("llmCall", shouldContinue, ["toolNode", END])
  .addEdge("toolNode", "llmCall")
  .compile();

// Invoke
import { HumanMessage } from "@langchain/core/messages";
const result = await agent.invoke({
  messages: [new HumanMessage("Add 3 and 4.")],
});

for (const message of result.messages) {
  console.log(`[${message.getType()}]: ${message.text}`);
}
```

:::

Using the Functional API:
:::python

```python
# Step 0: Define tools and model

from langchain_core.tools import tool
from langchain.chat_models import init_chat_model

llm = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    temperature=0
)


# Define tools
@tool
def multiply(a: int, b: int) -> int:
    """Multiply a and b.

    Args:
        a: first int
        b: second int
    """
    return a * b


@tool
def add(a: int, b: int) -> int:
    """Adds a and b.

    Args:
        a: first int
        b: second int
    """
    return a + b


@tool
def divide(a: int, b: int) -> float:
    """Divide a and b.

    Args:
        a: first int
        b: second int
    """
    return a / b


# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
llm_with_tools = llm.bind_tools(tools)

from langgraph.graph import add_messages
from langchain_core.messages import (
    SystemMessage,
    HumanMessage,
    BaseMessage,
    ToolCall,
)
from langgraph.func import entrypoint, task

# Step 1: define model node
@task
def call_llm(messages: list[BaseMessage]):
    """LLM decides whether to call a tool or not"""
    return llm_with_tools.invoke(
        [
            SystemMessage(
                content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
            )
        ]
        + messages
    )


# Step 2: define tool node
@task
def call_tool(tool_call: ToolCall):
    """Performs the tool call"""
    tool = tools_by_name[tool_call["name"]]
    return tool.invoke(tool_call)


# Step 3: define agent
@entrypoint()
def agent(messages: list[BaseMessage]):
    llm_response = call_llm(messages).result()

    while True:
        if not llm_response.tool_calls:
            break

        # Execute tools
        tool_result_futures = [
            call_tool(tool_call) for tool_call in llm_response.tool_calls
        ]
        tool_results = [fut.result() for fut in tool_result_futures]
        messages = add_messages(messages, [llm_response, *tool_results])
        llm_response = call_llm(messages).result()

    messages = add_messages(messages, llm_response)
    return messages

# Invoke
messages = [HumanMessage(content="Add 3 and 4.")]
for chunk in agent.stream(messages, stream_mode="updates"):
    print(chunk)
    print("\n")
```

:::

:::js

```typescript
// Step 0: define tools and model
import { ChatAnthropic } from "@langchain/anthropic";
import { tool } from "@langchain/core/tools";
import * as z from "zod";

const llm = new ChatAnthropic({
  model: "claude-3-7-sonnet-latest",
  temperature: 0,
});

// Define tools
const add = tool(({ a, b }) => a + b, {
  name: "add",
  description: "Add two numbers",
  schema: z.object({
    a: z.number().describe("First number"),
    b: z.number().describe("Second number"),
  }),
});

const multiply = tool(({ a, b }) => a * b, {
  name: "multiply",
  description: "Multiply two numbers",
  schema: z.object({
    a: z.number().describe("First number"),
    b: z.number().describe("Second number"),
  }),
});

const divide = tool(({ a, b }) => a / b, {
  name: "divide",
  description: "Divide two numbers",
  schema: z.object({
    a: z.number().describe("First number"),
    b: z.number().describe("Second number"),
  }),
});

// Augment the LLM with tools
const toolsByName = {
  [add.name]: add,
  [multiply.name]: multiply,
  [divide.name]: divide,
};
const tools = Object.values(toolsByName);
const llmWithTools = llm.bindTools(tools);

// Step 1: Define model node
import { task, entrypoint } from "@langchain/langgraph";
import { SystemMessage } from "@langchain/core/messages";
const callLlm = task({ name: "callLlm" }, async (messages: BaseMessage[]) => {
  return llmWithTools.invoke([
    new SystemMessage(
      "You are a helpful assistant tasked with performing arithmetic on a set of inputs."
    ),
    ...messages,
  ]);
});

// Step 2: define tool node
import type { ToolCall } from "@langchain/core/messages/tool";
const callTool = task({ name: "callTool" }, async (toolCall: ToolCall) => {
  const tool = toolsByName[toolCall.name];
  return tool.invoke(toolCall);
});

// Step 3: define agent
import { addMessages } from "@langchain/langgraph";
import { type BaseMessage, isAIMessage } from "@langchain/core/messages";
const agent = entrypoint({ name: "agent" }, async (messages: BaseMessage[]) => {
  let llmResponse = await callLlm(messages);

  while (true) {
    if (!llmResponse.tool_calls?.length) {
      break;
    }

    // Execute tools
    const toolResults = await Promise.all(
      llmResponse.tool_calls.map((toolCall) => callTool(toolCall))
    );
    messages = addMessages(messages, [llmResponse, ...toolResults]);
    llmResponse = await callLlm(messages);
  }

  return messages;
});

// Invoke
import { HumanMessage } from "@langchain/core/messages";
const result = await agent.invoke([new HumanMessage("Add 3 and 4.")]);

for (const message of result) {
  console.log(`[${message.getType()}]: ${message.text}`);
}
```

:::
